{"action":"closed","issue":{"url":"https://api.github.com/repos/dmlc/xgboost/issues/2701","repository_url":"https://api.github.com/repos/dmlc/xgboost","labels_url":"https://api.github.com/repos/dmlc/xgboost/issues/2701/labels{/name}","comments_url":"https://api.github.com/repos/dmlc/xgboost/issues/2701/comments","events_url":"https://api.github.com/repos/dmlc/xgboost/issues/2701/events","html_url":"https://github.com/dmlc/xgboost/issues/2701","id":257026485,"number":2701,"title":"Can I add more features of data for training continuation?","user":{"login":"fcbruce","id":7030768,"avatar_url":"https://avatars3.githubusercontent.com/u/7030768?v=4","gravatar_id":"","url":"https://api.github.com/users/fcbruce","html_url":"https://github.com/fcbruce","followers_url":"https://api.github.com/users/fcbruce/followers","following_url":"https://api.github.com/users/fcbruce/following{/other_user}","gists_url":"https://api.github.com/users/fcbruce/gists{/gist_id}","starred_url":"https://api.github.com/users/fcbruce/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/fcbruce/subscriptions","organizations_url":"https://api.github.com/users/fcbruce/orgs","repos_url":"https://api.github.com/users/fcbruce/repos","events_url":"https://api.github.com/users/fcbruce/events{/privacy}","received_events_url":"https://api.github.com/users/fcbruce/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":4,"created_at":"2017-09-12T12:32:41Z","updated_at":"2017-09-13T04:31:34Z","closed_at":"2017-09-13T04:31:34Z","author_association":"NONE","body":"As title, can I append some features to train a new model base on an old model?  \r\n\r\nFor example, I trained a model with features [A, B, C, D, E] as an old model. After some months, I collect more data and features and want to update the model. I continue to train a new model with features [A, B, C, D, E, F] base on the old one.\r\n\r\nI noticed I can pass the parameter `xgb_model` for training continuation, such as (python)\r\n```\r\nbst = xgb.train(params, d_train, num_round, watch_list, early_stopping_rounds=early_stopping, xgb_model=old_model_path)\r\n```\r\nThe code can run, but I'm not sure that appending features is correct on algorithm."}}
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